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SOAP: Spherical Overdensity and Aperture Processor

This repository contains programs which can be used to compute properties of halos in spherical apertures in SWIFT snapshots and to match halos between simulations using the particle IDs.

The code is written in python and uses mpi4py for parallelism.

Running on cosma

The files in the scripts directory are made for running on cosma. All scripts should be run from the base SOAP directory. Before running SOAP you should first create a python environment with ./scripts/cosma_python_env.sh

Computing halo membership for particles in the snapshot

The first program, group_membership.py, will compute bound halo indexes for all particles in a snapshot. The output consists of the same number of files as the snapshot with particle halo indexes written out in the same order as the snapshot.

Computing halo properties

The second program, compute_halo_properties.py, reads the simulation snapshot and the output from group_membership.py and uses it to calculate halo properties. It works as follows:

The simulation volume is split into chunks. Each compute node reads in the particles in one chunk at a time and calculates the properties of all halos in that chunk.

Within a compute node there is one MPI process per core. The particle data and halo catalogue for the chunk are stored in shared memory. Each core claims a halo to process, locates the particles in a sphere around the halo, and calculates the required properties. When all halos in the chunk have been done the compute node will move on to the next chunk.

Parameter files

To run either of the programs a parameters file must be passed. This contains information including the input and output directories, the halo finder to use, which halo definitions to use, and which properties to calculate for each halo definition. Example parameter files can be found in the parameters_files directory.

Compression

Two types of compression are useful for reducting the size of SOAP output. The first is lossless compression via GZIP, the second is lossy compression. For the group membership files we only apply lossless compression. However, each property in the final SOAP catalogue has a lossy compression filter associated with it, which are set in property_table.py. The script compression/compress_fast_metadata.py will apply both lossy and lossless compression to SOAP catalogues.

Usage on COSMA

Required modules

The same MPI module which was used to compile mpi4py must be loaded:

module load python/3.10.1 gnu_comp/11.1.0 openmpi/4.1.1

Calculating particle group membership

To run the group membership program needs the name of the simulation, the snapshot number, and a parameter file. For example:

snapnum=0077
sim=L1000N0900/DMO_FIDUCIAL
mpirun python3 -u -m mpi4py ./group_membership.py \
    --sim-name=${sim} --snap-nr${snapnum} parameter_files/FLAMINGO.yml

See scripts/FLAMINGO/L1000N1800/group_membership_L1000N1800.sh for an example batch script.

The code can optionally also write group membership to a single file virtual snapshot specified with the --update-virtual-file flag. This can be used to create a single file snapshot with group membership included that can be read with swiftsimio or gadgetviewer.

The --output-prefix flag can be used to specify a prefix used to name the datasets written to the virtual file. This may be useful if writing group membership from several different VR runs to a single file.

Calculating halo properties

To calculate halo properties you must pass the same information as for group membership. If the run is dark matter only the flag --dmo should be passed. For example:

snapnum=0077
sim=L1000N0900/DMO_FIDUCIAL
mpirun python3 -u -m mpi4py ./compute_halo_properties.py \
       --sim-name=${sim} --snap-nr=${snapnum} --chunks=1 ${dmo_flag} \
       parameter_files/FLAMINGO.yml

Here, --chunks determines how many chunks the simulation box is split into. Ideally it should be set such that one chunk fills a compute node.

The --max-ranks-reading flag determines how many MPI ranks per node read the snapshot. This can be used to avoid overloading the file system. The default value is 32.

Batch scripts for running on FLAMINGO simulations on Cosma-8

There are slurm scripts to run on FLAMINGO in ./scripts/FLAMINGO/. These are intended to be run as array jobs where the job array indexes determine which snapshots to process.

In order to reduce duplication only one script is provided per simulation box size and resolution. The simulation to process is specified by setting the job name with the slurm sbatch -J flag.

Adding quantities

The property calculations are defined in these files:

  • Properties of particles in halos subhalo_properties.py
  • Properties of particles in spherical apertures aperture_properties.py
  • Properties of particles in projected apertures projected_aperture_properties.py
  • Properties of particles in spheres of a specified overdensity SO_properties.py

Adding new quantities to already defined SOAP apertures is a relatively easy business. There are five steps.

  • Start by adding an entry to the property table (https://github.com/SWIFTSIM/SOAP/blob/master/property_table.py). Here we store all the properties of the quantities (name, type, unit etc.) All entries in this table are checked with unit tests and added to the documentation. Adding your quantity here will make sure the code and the documentation are in line with each other.
  • Next you have to add the quantity to the type of aperture you want it to be calculated for (aperture_properties.py, SO_properties.py, subhalo_properties.py or projected_aperture_properties.py). In all these files there is a class named property_list which defines the subset of all properties that are calculated for this specific aperture.
  • To calculate your quantity you have to define a @lazy_property with the same name in the XXParticleData class in the same file. There should be a lot of examples of different quantities that are already calculated. An important thing to note is that fields that are used for multiple calculations should have their own @lazy_property to avoid loading things multiple times, so check if the things that you need are already there.
  • Add the property to the parameter file, though if a property is missing from the parameter file then SOAP will calculate it by default.
  • At this point everything should now work. To test the newly added quantities you can run a unit test using python3 -W error -m pytest NAME_OF_FILE. This checks whether the code crashes, and whether there are problems with units and overflows. This should make sure that SOAP never crashes while calculating the new properties.

If SOAP does crash while evaluating your new property it will try to output the ID of the halo it was processing when it crashed. Then you can re-run that halo on a single MPI rank in the python debugger as described in the debugging section below.

Units

All particle data are stored in unyt arrays internally. On opening the snapshot a unyt UnitSystem is defined which corresponds to the simulation units. When particles are read in unyt arrays are created with units based on the attributes in the snapshot. These units are propagated through the halo property calculations and used to write the unit attributes in the output.

Comoving quantities are handled by defining a dimensionless unit corresponding to the expansion factor a.

Tests

The command ./tests/run_tests.sh will run the unit tests for SOAP. Some tests rely on data stored on cosma, and therefore cannot be run from other systems.

The scripts in tests/FLAMINGO for showing how to run SOAP on a few halos from the FLAMINGO simulations.

Debugging

For debugging it might be helpful to run on one MPI rank in the python debugger and reduce the run time by limiting the number of halo to process with the --max-halos flag:

python3 -Werror -m pdb ./compute_halo_properties.py --max-halos=10 ...

This works with OpenMPI at least, which will run single rank jobs without using mpirun.

The -Werror flag is useful for making pdb stop on warnings. E.g. division by zero in the halo property calculations will be caught.

It is also possible to select individual halos to process with the --halo-indices flag. This specifies the index of the required halos in the halo catalogue. E.g.

python3 -Werror -m pdb ./compute_halo_properties.py --halo-indices 1 2 3 ...

Profiling

The code can be profiled by running with the --profile flag, which uses the python cProfile module. Use --profile=1 to profile MPI rank zero only or --profile=2 to generate profiles for all ranks. This will generate files profile.N.txt with a text summary and profile.N.dat with data which can be loaded into profiling tools.

The profile can be visualized with snakeviz, for example. Usage on Cosma with x2go or VNC:

pip install snakeviz --user
snakeviz -b "firefox -no-remote %s" ./profile.0.dat

Matching halos between VR outputs

Note that this requires the latest version of https://github.com/jchelly/VirgoDC

This repository also contains a program to find halos which contain the same particle IDs between two outputs. It can be used to find the same halos between different snapshots or between hydro and dark matter only simulations.

For each halo in the first output we find the N most bound particle IDs and determine which halo in the second output contains the largest number of these IDs. This matching process is then repeated in the opposite direction and we check for cases were we have consistent matches in both directions.

Running the program

It can be run as follows:

vr_basename1="./vr/catalogue_0012/vr_catalogue_0012"
vr_basename2="./vr/catalogue_0013/vr_catalogue_0013"

outfile="halo_matching_0012_to_0013.hdf5"
nr_particles=10

mpirun python3 -u -m mpi4py \
    ./match_vr_halos.py ${vr_basename1} ${vr_basename2} \
    ${nr_particles} ${outfile} --use-types 0 1 2 3 4 5

Here nr_particles is the number of most bound particles to use for matching.

Matching using only specified particle types

The --use-types flag specifies which particle types to use for matching using the type numbering scheme from Swift. Only the specified types are included in the most bound particles used to match halos between snapshots. For example, --use-types 1 will cause the code to track the nr_particles most bound dark matter particles from each halo.

Matching to field halos only

The --to-field-halos-only flag can be used to match field halos (those with hostHaloID=-1 in the VR output) between outputs. If it is set we follow the first nr_particles most bound particles from each halo as usual, but when locating them in the other output any particles in halos with hostHaloID>=0 are treated as belonging to the host halo.

In this mode field halos in one catalogue will only ever be matched to field halos in the other catalogue.

Output is still generated for non-field halos. These halos will be matched to the field halo which contains the largest number of their nr_particles most bound particles. These matches will never be consistent in both directions because matches to non-field halos are not possible.

Output

The output is a HDF5 file with the following datasets:

  • BoundParticleNr1 - number of bound particles in each halo in the first catalogue
  • MatchIndex1to2 - for each halo in the first catalogue, index of the matching halo in the second
  • MatchCount1to2 - how many of the most bound particles from the halo in the first catalogue are in the matched halo in the second
  • Consistent1to2 - whether the match from first to second catalogue is consistent with second to first (1) or not (0)

There are corresponding datasets with 1 and 2 reversed with information about matching in the opposite direction.

Documentation

PDF document

A pdf describing the SOAP output can be generated. First run property_table.py passing the parameter file used to run SOAP, e.g. python property_table.py parameter_files/FLAMINGO.yml. This will generate a table containing all the properties which are enabled in the parameter file. To create the pdf run pdflatex documentation/SOAP.tex.

API reference

Most of the files containing halo property calculations have been extensively documented using docstrings. To generate documentation, you can for example use

python3 -m pydoc aperture_properties

This will display the documentation for the file aperture_properties.py.

python3 -m pydoc -b

will display an interactive web page in your browser with a lot of documentation, including all documented files and functionality of SOAP.

Please have a look at this documentation before making any significant changes!